Prediction of Likelihood of Cancer Recurrence

ABSTRACT

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of cancer, in particular of breast cancer.

The present application claims the benefit under 35 U.S.C. 119(e) of thefiling date of U.S. Application Ser. No. 60/482,339, filed on Jun. 24,2003.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention provides gene sets the expression of which isimportant in the diagnosis and/or prognosis of cancer.

Description of the Related Art

Oncologists have a number of treatment options available to them,including different combinations of chemotherapeutic drugs that arecharacterized as “standard of care,” and a number of drugs that do notcarry a label claim for particular cancer, but for which there isevidence of efficacy in that cancer. Best likelihood of good treatmentoutcome requires that patients be assigned to optimal available cancertreatment, and that this assignment be made as quickly as possiblefollowing diagnosis.

Currently, diagnostic tests used in clinical practice are singleanalyte, and therefore do not capture the potential value of knowingrelationships between dozens of different markers. Moreover, diagnostictests are frequently not quantitative, relying on immunohistochemistry.This method often yields different results in different laboratories, inpart because the reagents are not standardized, and in part because theinterpretations are subjective and cannot be easily quantified.RNA-based tests have not often been used because of the problem of RNAdegradation over time and the fact that it is difficult to obtain freshtissue samples from patients for analysis. Fixed paraffin-embeddedtissue is more readily available and methods have been established todetect RNA in fixed tissue. However, these methods typically do notallow for the study of large numbers of genes (DNA or RNA) from smallamounts of material. Thus, traditionally fixed tissue has been rarelyused other than for immunohistochemistry detection of proteins.

In the past few years, several groups have published studies concerningthe classification of various cancer types by microarray gene expressionanalysis (see, e.g. Golub et al., Science 286:531-537 (1999);Bhattacharjae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001);Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001);Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001)).Certain classifications of human breast cancers based on gene expressionpatterns have also been reported (Martin et al., Cancer Res.60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA98:11462-11467 (2001); Sorlic et al., Proc. Natl. Acad. Sci. USA98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)).However, these studies mostly focus on improving and refining thealready established classification of various types of cancer, includingbreast cancer, and generally do not provide new insights into therelationships of the differentially expressed genes, and do not link thefindings to treatment strategies in order to improve the clinicaloutcome of cancer therapy.

Although modern molecular biology and biochemistry have revealedhundreds of genes whose activities influence the behavior of tumorcells, state of their differentiation, and their sensitivity orresistance to certain therapeutic drugs, with a few exceptions, thestatus of these genes has not been exploited for the purpose ofroutinely making clinical decisions about drug treatments. One notaaleexception is the use of estrogen receptor (ER) protein expression inbreast carcinomas to select patients to treatment with anti-estrogendrugs, such as tamoxifen. Another exceptional example is the use ofErbB2 (Her2) protein expression in breast carcinomas to select patientswith the Her2 antagonist drug Herceptin® (Genentech, Inc., South SanFrancisco, Calif.).

Despite recent advances, the challenge of cancer treatment remains totarget specific treatment regimens to pathogenically distinct tumortypes, and ultimately personalize tumor treatment in order to maximizeoutcome. Hence, a need exists for tests that simultaneously providepredictive information about patient responses to the variety oftreatment options. This is particularly true for breast cancer, thebiology of which is poorly understood. It is clear that theclassification of breast cancer into a few subgroups, such as ErbB2⁺subgroup, and subgroups characterized by low to absent gene expressionof the estrogen receptor (ER) and a few additional transcriptionalfactors (Perou et al., Nature 406:747-752 (2000)) does not reflect thecellular and molecular heterogeneity of breast cancer, and does notallow the design of treatment strategies maximizing patient response.

In particular, once a patient is diagnosed with cancer, such as breastor ovarian cancer, there is a strong need for methods that allow thephysician to predict the expected course of disease, including thelikelihood of cancer recurrence, long-term survival of the patient, andthe like, and select the most appropriate treatment option accordingly.

SUMMARY OF THE INVENTION

The present invention provides a set of genes, the expression of whichhas prognostic value, specifically with respect to disease-freesurvival.

The present invention accommodates the use of archived paraffin-embeddedbiopsy material for assay of all markers in the set, and therefore iscompatible with the most widely available type of biopsy material. It isalso compatible with several different methods of tumor tissue harvest,for example, via core biopsy or fine needle aspiration. Further, foreach member of the gene set, the invention specifies oligonucleotidesequences that can be used in the test.

In one aspect, the present invention concerns a method of predicting thelikelihood of long-term survival of a cancer patient without therecurrence of cancer, comprising determining the expression level of oneor more prognostic RNA transcripts or their expression products in acancer cell obtained from the patient, normalized against the expressionlevel of all RNA transcripts or their products in said cancer cell, orof a reference set of RNA transcripts or their expression products,wherein the prognostic RNA transcript is the transcript of one or moregenes selected from the group consisting of B_Catenin; BAG1; BIN1; BUB1;C20_orf1; CCNB1; CCNE2; CDC20; CDH1; CEGP1; CIAP1; cMYC; CTSL2;DKFZp586M07; DR5; EpCAM; EstR1; FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB;ID1; IGF1R; ITGA7; Ki_67; KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2;NEK2; NME1; NPD009; PCNA; PR; PREP; PTTG1; RPLPO; Src; STK15; STMY3;SURV; TFRC; TOP2A; and TS;

wherein expression of one or more of BUB1; C20_orf1; CCNB1; CCNE2;CDC20; CDH1; CTSL2; EpCAM; FOXM1; GRB7; HER2; HNRPAB; Ki_67; KNSL2;LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2; NEK2; NME1; PCNA; PREP; PTTG1;Src; STK15; STMY3; SURV; TFRC; TOP2A; and TS indicates a decreasedlikelihood of long-term survival without cancer recurrence; and

the expression of one or more of BAG1; BCatenin; BIN1; CEGP1; CIAP1;cMYC; DKFZp586M07; DR5; EstR1; GSTM1; GSTM3; ID1; IGFIR; ITGA7; NPD009;PR; and RPLPO indicates an increased likelihood of long-term survivalwithout cancer recurrence.

In various embodiments, the expression level of at least 2, or at least5, or at least 10, or at least 15, or at least 20, or a least 25prognostic RNA transcripts or their expression products is determined.

In another embodiment, the cancer is breast cancer or ovarian cancer.

In yet another embodiment, the cancer is node negative, ER positivebreast cancer.

In a further embodiment, the RNA comprises intronic RNA.

In a still further embodiment, the expression level of one or moreprognostic RNA transcripts or their expression products of one or moregenes selected from the group consisting of MMP9, GSTM1, MELK, PR,DKFZp586M07, GSTM3, CDC20, CCNB1, STMY3, GRB7, MYBL2, CEGP1, SURV,LMNB1, CTSL2, PTTG1, BAG1, KNSL2, CIAP1, PREP, NEK2, EpCAM, PCNA,C20_orf1, ITGA7, ID1 B_Catenin, EstR1 CDH1, TS HER2, and cMYC isdetermined,

wherein expression of one or more of C20_orf1; CCNB1; CDC20; CDH1;CTSL2; EpCAM; GRB7; HER2; KNSL2; LMNB1; MCM2; MMP9; MYBL2; NEK2; PCNA;PREP; PTTG1; STMY3; SURV; TS; and MELK indicates a decreased likelihoodof long-term survival without cancer recurrence; and

the expression of one or more of BAG1; BCrtenin; CEGP1; CIAP1; cMYC;DKFZp586MO7; EstR1; GSTM1; GSTM3; ID1; ITGA7; and PR indicates anincreased likelihood of long-term survival without cancer recurrence.

In another embodiment, the expression level of one or more prognosticRNA transcripts or their expression products of one or more genesselected from the group consisting of GRB7, SURV, PR, LMNB1, MYBL2,HER2, GSTM1, MELK, S20_orf1, PTTG1, BUB1, CDC20, CCNB1, STMY3, KNSL2,CTSL2, MCM2, NEK2, DR5, Ki_67, CCNE2, TOP2A, PCNA, PREP, FOXM1, NME1,CEGP1, BAG1, STK15, HNRPAB, EstR1, MMP9, DKFZp586MO7, TS, Src, BIN1,NP009, RPLPO, GSTM3, MMP12, TFRC, and IGF1R is determined,

wherein expression of one or more of GRB7; SURV; LMNB1; MYBL2; HER2;MELK; C20 orf1; PTTG1; BUB1; CDC20; CCNB1; STMY3; KNSL2; CTSL2; MCM2;NEK2; Ki_67; CCNE2; TOP2A_4; PCNA; PREP; FOXM1; NME1; STK15; HNRPAB;MMP9; TS; Src; MMPI2; and TFRC indicates a decreased likelihood oflong-term survival without cancer recurrence; and

the expression of one or more of PR; GSTM1; DR5; CEGP1; BAG1; EstR1;DKFZp586MO7; BIN1; NPD009; RPLPO; GSTM3; IGFIR indicates an increasedlikelihood of long-term survival without cancer recurrence.

In another aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a cancer patient without therecurrence of cancer, comprising determining the expression level of oneor more prognostic RNA transcripts or their expression products in acancer cell obtained from said patient, normalized against theexpression level of all RNA transcripts or their products in the cancercell, or of a reference set of RNA transcripts or their expressionproducts, wherein the prognostic RNA transcript is the transcript of oneor more genes selected from the group consisting of GRB7; LMNB1; ER;STMY3; KLK10; PR; KRT5; FGFR1; MCM6; SNRPF,

wherein expression of one or more of GRB7, LMNB1, STMY3, KLK10, FGFR1,and SNRPF indicates a decreased likelihood or long term survival withoutcancer recurrence; and the expression of one or more of ER, PR, KRT5 andMCM6 ER, PR, KRT5 and MCM6 indicates an increased likelihood oflong-term survival without cancer recurrence.

In an embodiment of this method, the RNA is isolated from a fixed,wax-embedded breast cancer tissue specimen of the patient.

In another embodiment, the RNA is isolated from core biopsy tissue orfine needle aspirate cells.

In a different aspect, the invention concerns an array comprisingpolynucleotides hybridizing to two or more of the following genes:B_Catenin; BAG1; BIN1; BUB1; C20_orf1; CCNB1; CCNE2; CDC20; CDH1; CEGP1;CIAP1; cMYC; CTSL2; DKFZp586MO7; DR5; EpCAM; EstR1; FOXM1; GRB7; GSTM1;GSTM3; HER2; HNRPAB; ID1; IGFIR; ITGA7; Ki_67; KNSL2; LMNB1; MCM2; MELK;MMP12; MMP9; MYBL2; NEK2; NME1; NPD009; PCNA; PR; PREP; PTTG1; RPLPO;Src; STK15; STMY3; SURV; TFRC; TOP2A; and TS, immobilized on a solidsurface.

In an embodiment, the array comprises polynucleotides hybridizing to twoor more of the following genes: MMP9, GSTM1, MELK, PR, DKFZp586MO7,GSTM3, CDC20, CCNB1, STMY3, GRB7, MYBL2, CEGP1, SURV, LMNB1, CTSL2,PTTG1, BAG1, KNSL2, CIAP1, PREP, NEK2, EpCAM, PCNA, C20_orf1, ITGA7, ID1B_Catenin, EstR1, CDH1, TS HER2, and cMYC.

In another embodiment, the array comprises polynucleotides hybridizingto two or more of the following genes: GRB7, SURV, PR, LMNB1, MYBL2,HER2, GSTM1, MELK, S20 orf1, PTTG1, BUB1, CDC20, CCNB1, STMY3, KNSL2,CTSL2, MCM2, NEK2, DR5, Ki_67, CCNE2, TOP2A, PCNA, PREP, FOXM1, NME1,CEGP1, BAG1, STK15, HNRPAB, EstR1, MMP9, DKFZp586M07, TS, Src, BIN1,NP009, RPLPO, GSTM3, MMP12, TFRC, and IGFIR.

In a further embodiment, the arrays comprise polynucleotides hybridizingto at least 3, or at least 5, or at least 10, qr at least 15, or atleast 20, or at least 25 of the listed genes.

In a still further embodiment, the arrays comprise polynucleotideshybridizing to all of the listed genes.

In yet another embodiment, the arrays comprise more than onepolynucleotide hybridizing to the same gene.

In an additional embodiment, the arrays comprise intron-based sequences.

In another embodiment, the polynucleotides are cDNAs, which can, forexample, be about 500 to 5000 bases long.

In yet another embodiment, the polynucleotides are oligonucleotides,which can, for example, be about 20 to 80 bases long.

The arrays can, for example, be immobilized on glass, and can containhundreds of thousand, e.g. 330,000 oligonucleotides.

In a further aspect, the invention concerns a method of predicting thelikelihood of long-term survival of a patient diagnosed with invasivebreast cancer, without the recurrence of breast cancer, comprising thesteps of

(a) determining the expression levels of the RNA transcripts or theexpression products of genes of a gene set selected from the groupconsisting of B_Catenin; BAG1; BIN1; BUB1; C20_orf1; CCNB1; CCNE2;CDC20; CDH1; CEGI1; CIAP1; cMYC; CTSL2; DKFZp586M07; DR5; EpCAM; EstR1;FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB; ID1; IGFlR; ITGA7; Ki_67;KNSL2; LMNB1; MCM2; MELK; MMP12; MMP9; MYBL2; NEK2; NME1; NPD009; PCNA;PR; PREP; PTTG1; RPLPO; Src; STK15; STMY3; SURV; TFRC; TOP2A; and TS inabreast cancer cell obtained from the patient, normalized against theexpression levels of all RNA transcripts or their expression products insaid breast cancer cell, or of a reference set of RNA transcripts ortheir products;

(b) subjecting the data obtained in step (a) to statistical analysis;and;

(c) determining whether the likelihood of said long-term survival hasincreased or decreased.

In a still further aspect, the invention concerns a method of preparinga personalized genomics profile for a patient, comprising the steps of

(a) subjecting RNA extracted from a breast tissue obtained from thepatient to gene expression analysis;

(b) determining the expression level in the tissue of one or more genesselected from the breast cancer gene set listed in any one of Tables 1and 2, wherein the expression level is normalized against a control geneor genes and optionally is compared to the amount found in a breastcancer reference tissue set; and

(c) creating a report summarizing the data obtained by said geneexpression analysis.

The breast tissue may comprise breast cancer cells.

In another embodiment, the breast tissue is obtained from a fixed,paraffin-embedded biopsy sample, in which the RNA may be fragmented.

The report may include prediction of the likelihood of long termsurvival of the patient and/or a recommendation for a treatment modalityof said patient.

In a further aspect, the invention concerns a method for measuringlevels of mRNA products of genes listed in Tables 1 and 2 by real timepolymerase chain reaction (RT-PCR), by using an amplicon listed in Table3 and a primer-probe set listed in Tables 4A-4D.

In a still further aspect, the invention concerns a PCR primer-probe setlisted in Tables 4A-4D, and a PCR amplicon listed in Table 3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT A. Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Singleton et al., Dictionary ofMicrobiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York,N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanismsand Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provideone skilled in the art with a general guide to many of the terms used inthe present application.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein, which could be used in thepractice of the present invention. Indeed, the present invention is inno way limited to the methods and materials described. For purposes ofthe present invention, the following terms are defined below.

The term “microarray” refers to an ordered arrangement of hybridizablearray elements, preferably polynucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural, generallyrefers to any polyribonucleotide or polydeoxribonucleotide, which may beunmodified RNA or DNA or modified RNA or DNA. Thus, for instance,polynucleotides as defined herein include, without limitation, single-and double-stranded DNA, DNA including single- and double-strandedregions, single- and double-stranded RNA, and RNA including single- anddouble-stranded regions, hybrid molecules comprising DNA and RNA thatmay be single-stranded or, more typically, double-stranded or includesingle- and double-stranded regions. In addition, the term“polynucleotide” as used herein refers to triple-stranded regionscomprising RNA or DNA or both RNA and DNA. The strands ii such regionsmay be from the same molecule or from different molecules. The regionsmay include all of one or more of the molecules, but more typicallyinvolve only a region of some of the molecules. One of the molecules ofa triple-helical region often is an oligonucleotide. The term“polynucleotide” specifically includes cDNAs. The term includes DNAs(including cDNAs) and RNAs that contain one or more modified bases.Thus, DNAs or RNAs with backbones modified for stability or for otherreasons are “polynucleotides” as that term is intended herein. Moreover,DNAs or RNAs comprising unusual bases, such as inosine, or modifiedbases, such as tritiated bases, are included within the term“polynucleotides” as defined herein. In general, the term“polynucleotide” embraces all chemically, enzymatically and/ormetabolically modified forms of unmodified polynucleotides, as well asthe chemical forms of DNA and RNA characteristic of viruses and cells,including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide,including, without limitation, single-stranded deoxyribonucleotides,single- or double-stranded ribonucleotides, RNA:DNA hybrids anddouble-stranded DNAs. Oligonucleotides, such as single-stranded DNAprobe oligonucleotides, are often synthesized by chemical methods, forexample using automated oligonucleotide synthesizers that arecommercially available. However, oligonucleotides can be made by avariety of other methods, including in vitro recombinant DNA-mediatedtechniques and by expression of DNAs in cells and organisms.

The terms “differentially expressed gene,” “differential geneexpression” and their synonyms, which are used interchangeably, refer toa gene whose expression is activated to a higher or lower level in asubject suffering from a disease, specifically cancer, such as breastcancer, relative to its expression in a normal or control subject. Theterms also include genes whose expression is activated to a higher orlower level at different stages of the same disease. It is alsounderstood that a differentially expressed gene may be either activatedor inhibited at the nucleic acid level or protein level, or may besubject to alternative splicing to result in a different polypeptideproduct. Such differences may be evidenced by a change in mRNA levels,surface expression, secretion or other partitioning of a polypeptide,for example. Differential gene expression may include a comparison ofexpression between two or more genes or their gene products, or acomparison of the ratios of the expression between two or more genes ortheir gene products, or even a comparison of two differently processedproducts of the same gene, which differ between normal subjects and,subjects suffering from a disease, specifically cancer, or betweenvarious stages of the same disease. Differential expression includesboth quantitative, as well as qualitative, differences in the temporalor cellular expression pattern in a gene or its expression productsamong, for example, normal and diseased cells, or among cells which haveundergone different disease events or disease stages. For the purpose ofthis invention, “differential gene expression” is considered to bepresent when there is at least an about two-fold, preferably at leastabout four-fold, more preferably at least about six-fold, mostpreferably at least about ten-fold difference between the expression ofa given gene in normal and diseased subjects, or in various stages ofdisease development in a diseased subject.

The term “over-expression” with regard to an RNA transcript is used torefer to the level of the transcript determined by normalization to thelevel of reference mRNAs, which might be all measured transcripts in thespecimen or a particular reference set of mRNAs.

The phrase “gene amplification” refers to a process by which multiplecopies of a gene or gene fragment are formed in a particular cell orcell line. The duplicated region (a stretch of amplified DNA) is oftenreferred to as “amplicon.” Usually, the amount of the messenger RNA(mRNA) produced, i.e., the level of gene expression, also increases inthe proportion of the number of copies made of the particular geneexpressed.

The term “prognosis” is used herein to refer to the prediction of thelikelihood of cancer-attributable death or progression, includingrecurrence, metastatic spread, and drug resistance, of a neoplasticdisease, such as breast cancer. The term “prediction” is used herein torefer to the likelihood that a patient will respond either favorably orunfavorably to a drug or set of drugs, and also the extent of thoseresponses, or that a patient will survive, following surgical removal orthe primary tumor and/or chemotherapy for a certain period of timewithout cancer recurrence. The predictive methods of the presentinvention can be used clinically to make treatment decisions by choosingthe most appropriate treatment modalities for any particular patient.The predictive methods of the present invention are valuable tools inpredicting if a patient is likely to respond favorably to a treatmentregimen, such as surgical intervention, chemotherapy with a given drugor drug combination, and/or radiation therapy, or whether long-termsurvival of the patient, following surgery and/or termination ofchemotherapy or other treatment modalities is likely.

The term “long-term” survival is used herein to refer to survival for atleast 3 years, more preferably for at least 8 years, most preferably:for at least 10 years following surgery or other treatment.

The term “tumor,” as used herein, refers to all neoplastic cell growthand proliferation, whether malignant or benign, and all pre-cancerousand cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe thephysiological condition in mammals that is typically characterized byunregulated cell growth. Examples of cancer include, but are not limitedto, breast cancer, ovarian cancer, colon cancer, lung cancer, prostatecancer, hepatocellular cancer, gastric cancer, pancreatic cancer,cervical cancer, liver cancer, bladder cancer, cancer of the urinarytract, thyroid cancer, renal cancer, carcinoma, melanoma, and braincancer.

The “pathology” of cancer includes all phenomena that compromise thewell-being of the patient. This includes, without limitation, abnormalor uncontrollable cell growth, metastasis, interference with the normalfunctioning of neighboring cells, release of cytokines or othersecretory products at abnormal levels, suppression or aggravation ofinflammatory or immunological response, neoplasia, premalignancy,malignancy, invasion of surrounding or distant tissues or organs, suchas lymph nodes, etc.

“Stringency” of hybridization reactions is readily determinable by oneof ordinary skill in the art, and generally is an empirical calculationdependent upon probe length, washing temperature, and saltconcentration. In general, longer probes require higher temperatures forproper annealing, while shorter probes need lower temperatures.Hybridization generally depends on the ability of denatured DNA toreanneal when complementary strands are present in an environment belowtheir melting temperature. The higher the degree of desired homologybetween the probe and hybridizable sequence, the higher the relativetemperature which can be used. As a result, it follows that higherrelative temperatures would tend to make the reaction conditions morestringent, while lower temperatures less so. For additional details andexplanation of stringency of hybridization reactions, see Ausubel etal., Current Protocols in Molecular Biology. Wiley IntersciencePublishers, (1995).

“Stringent conditions” or “high stringency conditions”, as definedherein, typically: (1) employ low ionic strength and high temperaturefor washing, for example 0.015 M sodium chloride/0.0015 M sodiumcitrate/0.1% sodium dodecyl sulfate at 50° C.; (2) employ duringhybridization a denaturing agent, such as formamide, for example, 50%(v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1%polyvinylpyrrolidone/50 mM sodium phosphate buffer at pH 6.5 with 750 mMsodium chloride, 75 mM sodium citrate at 42° C.; or (3) employ 50%formamide, 5×SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodiumphosphate (pH 6.8), 0.1% sodium pyrophosphate, 5×Denhardt's solution,sonicated salmon sperm DNA (50 μg/ml), 0.1% SDS, and 10% dextran sulfateat 42° C., with washes at 42° C. in 0.2×SSC (sodium chloride/sodiumcitrate) and 50% formamide at 55° C., followed by a high-stringency washconsisting of 0.1×SSC containing EDTA at 55° C.

“Moderately stringent conditions” may be identified as described bySambrook et al., Molecular Cloning: A Laboratory Manual, New York: ColdSpring Harbor Press, 1989, and include the use of washing solution andhybridization conditions (e.g., temperature, ionic strength and % SDS)less stringent that those described above. An example of moderatelystringent conditions is overnight incubation at 37° C. in a solutioncomprising: 20% formamide, 5×SSC (150 mM NaCl, 15 mM trisodium citrate),50 mM sodium phosphate (pH 7.6), 5×Denhardt's solution, 10% dextransulfate, and 20 mg/ml denatured sheared salmon sperm DNA, followed bywashing the filters in 1×SSC at about 37-50° C. The skilled artisan willrecognize how to adjust the temperature, ionic strength, etc. asnecessary to accommodate factors such as probe length and the like.

In the context of the present invention, reference to “at least one,”“at least two,” “at least five,” etc. of the genes listed in anyparticular gene set means any one or any and all combinations of thegenes listed.

The term “node negative” cancer, such as “node negative” breast cancer,is used herein to refer to cancer that has not spread to the lymphnodes.

The terms “splicing” and “RNA splicing” are used interchangeably andrefer to RNA processing that removes introns and joins exons to producemature mRNA with continuous coding sequence that moves into thecytoplasm of an eukaryotic cell.

In theory, the term “exon” refers to any segment of an interrupted genethat is represented in the mature RNA product (B. Lewin. Genes IV CellPress, Cambridge Mass. 1990). In theory the term “intron” refers to anysegment of DNA that is transcribed but removed from within thetranscript by splicing together the exons on either side of it.Operationally, exon sequences occur in the mRNA sequence of a gene asdefined by Ref. SEQ ID numbers. Operationally, intron sequences are theintervening sequences within the genomic DNA of a gene, bracketed byexon sequences and having GT and AG splice consensus sequences at their5′ and 3′ boundaries.

B. Detailed Description

The practice of the present invention will employ, unless otherwiseindicated, conventional techniques of molecular biology (includingrecombinant techniques), microbiology, cell biology, and biochemistry,which are within the skill of the art. Such techniques are explainedfully in the literature, such as, “Molecular Cloning: A LaboratoryManual”, 2^(nd) edition (Sambrook et al., 1989); “OligonucleotideSynthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I.Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.);“Handbook of Experimental Immunology”, 4^(th) edition (D. M. Weir & C.C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene TransferVectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987);“Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds.,1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds.,1994).

1. Gene Expression Profiling

Methods of gene expression profiling include methods based onhybridization analysis of polynucleotides, methods based on sequencingof polynucleotides, and proteomics-based methods. The most commonly usedmethods known in the art for the quantification of mRNA expression in asample include northern blotting and in situ hybridization (Parker &Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAseprotection assays (Hod, Biotechniques 13:852-854 (1992)); and PCR-basedmethods, such as reverse transcription polymerase chain reaction(RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)).Alternatively, antibodies may be employed that can recognize specificduplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybridduplexes: or DNA-protein duplexes. Representative methods forsequencing-based gene expression analysis include Serial Analysis ofGene Expression (SAGE), and gene expression analysis by massivelyparallel signature sequencing (MPSS).

2. PCR-Based Gene Expression Profiling Methods

a. Reverse Transcriptase PCR (RT-PCR)

Of the techniques listed above, the most sensitive and most flexiblequantitative method is RT-PCR, which can be used to compare mRNA levelsin different sample populations, in normal and tumor tissues, with orwithout drug treatment, to characterize patterns of gene expression, todiscriminate between closely related mRNAs, and to analyze RNAstructure.

The first step is the isolation of mRNA from a target sample. Thestarting material is typically total RNA isolated from human tumors ortumor cell lines, and corresponding normal tissues or cell lines,respectively. Thus RNA can be isolated from a variety of primary tumors,including breast, lung, colon, prostate, brain, liver, kidney, pancreas,spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines,with pooled DNA from healthy donors. If the source of mRNA is a primarytumor, mRNA can be extracted, for example, from frozen or archivedparaffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art and aredisclosed in standard textbooks of molecular biology, including Ausubelet al., Current Protocols of Molecular Biolony. John Wiley and Sons(1997). Methods for RNA extraction from paraffin embedded tissues aredisclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987),and De Andrés et al., BioTechniques 18:42044 (1995). In particular, RNAisolation can be performed using purification kit, buffer set andprotease from commercial manufacturers, such as Qiagen, according to themanufacturer's instructions. For example, total RNA from cells inculture can be isolated using Qiagen RNeasy mini-columns. Othercommercially available RNA isolation kits include MasterPure™ CompleteDNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and ParaffinBlock RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samplescan be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumorcan be isolated, for example, by cesium chloride density gradientcentrifugation.

As RNA cannot serve as a template for PCR, the first step in geneexpression profiling by RT-PCR is the reverse transcription of the RNAtemplate into cDNA, followed by its exponential amplification in a PCRreaction. The two most commonly used reverse transcriptases are avilomyeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murineleukemia virus reverse transcriptase (MMLV-RT). The reversetranscription step is typically primed using specific primers, randomhexamers, or oligo-dT primers, depending on the circumstances and thegoal of expression profiling. For example, extracted RNA can bereverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA),following the manufacturer's instructions. The derived cDNA can then beused as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which hasa 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonucleaseactivity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activityof Taq or Tth polymerase to hydrolyze a hybridization probe bound to itstarget amplicon, but any enzyme with equivalent 5′ nuclease activity canbe used. Two oligonucleotide primers are used to generate an amplicontypical of a PCR reaction. A third oligonucleotide, or probe, isdesigned to detect nucleotide sequence located between the two PCRprimers. The probe is non-extendible by Taq DNA polymerase enzyme, andis labeled with a reporter fluorescent dye and a quencher fluorescentdye. Any laser-induced emission from the reporter dye is quenched by thequenching dye when the two dyes are located close together as they areon the probe. During the amplification reaction, the Taq DNA polymeraseenzyme cleaves the probe in a template-dependent manner. The resultantprobe fragments disassociate in solution, and signal from the releasedreporter dye is free from the quenching effect of the secondfluorophoret One molecule of reporter dye is liberated for each newmolecule synthesized, and detection of the unquenched reporter dyeprovides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In apreferred embodiment, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700™ Sequence DetectionSystem™. The system consists of a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system amplifies samples in a96-well format on a thermocycler. During amplification, laser-inducedfluorescent signal is collected in real-time through fiber optics cablesfor all 96 wells, and detected at the CCD. The system includes softwarefor running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the thresholdcycle. As discussed above, fluorescence values are recorded during everycycle and represent the amount of product amplified to that point in theamplification reaction. The point when the fluorescent signal is firstrecorded as statistically significant is the threshold cycle (C).

To minimize errors and the effect of sample-to-sample variation, RT-PCRis usually performed using an internal standard. The ideal internalstandard is expressed at a constant level among different tissues, andis unaffected by the experimental treatment. RNAs most frequently usedto normalize patterns of gene expression are mRNAs for the housekeepinggenes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin.

A more recent variation of the RT-PCR technique is the real timequantitative PCR, which measures PCR product accumulation through adual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR iscompatible both with quantitative competitive PCR, where internalcompetitor for each target sequence is used for normalization, and withquantitative comparative PCR using a normalization gene contained withinthe sample, or a housekeeping gene for RT-PCR. For further details see,e.g. Held et al., Genome Research 6:986-994 (1996).

The steps of a representative protocol for profiling gene expressionusing fixed, paraffin-embedded tissues as the RNA source, including mRNAisolation, purification, primer extension and amplification are given invarious published journal articles {for example: T. E. Godfrey et al. J.Molec. Diagnostics 2: 84-91 [2000]; K. Specht et al., Am. J. Pathol.158: 419-29 [2001]}. Briefly, a representative process starts withcutting about 10 upm thick sections of paraffin-embedded tumor tissuesamples. The RNA is then extracted, and protein and DNA are removed.After analysis of the RNA concentration, RNA repair and/or amplificationsteps may be included, if necessary, and RNA is reverse transcribedusing gene specific promoters followed by RT-PCR.

b. MassARRAY System

In the MassARRAY-based gene expression profiling method, developed bySequenom, Inc. (San Diego, Calif.) following the isolation of RNA andreverse transcription, the obtained cDNA is spiked with a synthetic DNAmolecule (competitor), which matches the targeted cDNA region in allpositions, except a single base, and serves as an internal standard. ThecDNA/competitor mixture is PCR amplified and is subjected to a post-PCRshrimp alkaline phosphatase (SAP) enzyme treatment, which results in thedephosphorylation of the remaining nucleotides. After inactivation ofthe alkaline phosphatase, the PCR products from the competitor and cDNAare subjected to primer extension, which generates distinct mass signalsfor the competitor- and cDNA-derives PCR products. After purification,these products are dispensed on a chip array, which is pre-loaded withcomponents needed for analysis with matrix-assisted laser desorptionionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. ThecDNA present in the reaction is then quantified by analyzing the ratiosof the peak areas in the mass spectrum generated. For further detailssee, e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059-3064(2003).

c. Other PCR-Based Methods

Further PCR-based techniques include, for example, differential display(Liang and Pardee, Science 257:967-971 (1992)); amplified fragmentlength polymorphism (iAFLP) (Kawamoto et al., Genome Res. 12:1305-1312(1999)); BeadArray™ technology (Illumina, San Diego, Calif.; Oliphant etal., Discovery of Markers for Disease (Supplement to Biotechniques),June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); ItBeadsArray for Detection of Gene Expression (BADGE), using thecommercially available Luminex100 LabMAP system and multiple color-codedmicrospheres (Luminex Corp., Austin, Tex.) in a rapid assay for geneexpression (Yang et al., Genome Res. 11:1888-1898 (2001)); and highcoverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl.Acids. Res. 31(16) e94 (2003)).

3. Microarrays

Differential gene expression can also be identified, or confirmed usingthe microarray technique. Thus, the expression profile of breastcancer-associated genes can be measured in either fresh orparaffin-embedded tumor tissue, using microarray technology. In thismethod, polynucleotide sequences of interest (including cDNAs andoligonucleotides) are plated, or arrayed, on a microchip substrate. Thearrayed sequences are then hybridized with specific DNA probes fromcells or tissues of interest. Just as in the RT-PCR method, the sourceof mRNA typically is total RNA isolated from human tumors or tumor celllines, and corresponding normal tissues or cell lines. Thus RNA can beisolated from a variety of primary tumors or tumor cell lines. If thesource of mRNA is a primary tumor, mRNA can be extracted, for example,from frozen or archived paraffin-embedded and fixed (e.g.formalin-fixed) tissue samples, which are routinely prepared andpreserved in everyday clinical practice.

In a specific embodiment of the microarray technique, PCR amplifiedinserts of cDNA clones are applied to a substrate in a dense array.Preferably at least 10,000 nucleotide sequences are applied to thesubstrate. The microarrayed genes, immobilized on the microchip at10,000 elements each, are suitable for hybridization under stringentconditions. Fluorescently labeled cDNA probes may be generated throughincorporation of fluorescent nucleotides by reverse transcription of RNAextracted from tissues of interest. Labeled cDNA probes applied to thechip hybridize with specificity to each spot of DNA on the array. Afterstringent washing to remove non-specifically bound probes, the chip isscanned by confocal laser microscopy or by another detection method,such as a. CCD camera. Quantitation of hybridization of each arrayedelement allows for assessment of corresponding mRNA abundance. With dualcolor fluorescence, separately labeled cDNA probes generated from twosources of RNA are hybridized pairwise to the array. The relativeabundance of the transcripts from the two sources corresponding to eachspecified gene is thus determined simultaneously. The miniaturized scaleof the hybridization affords a convenient and rapid evaluation of theexpression pattern for large numbers of genes. Such methods have beenshown to have the sensitivity required to detect rare transcripts, whichare expressed at a few copies per cell, and to reproducibly detect atleast approximately two-fold differences in the expression levels(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)).Microarray analysis can be performed by commercially availableequipment, following manufacturer's protocols, such as by using theAffymetrix GenChip technology, or Incyte's microarray technology.

The development of microarray methods for large-scale analysis of geneexpression makes it possible to search systematically for molecularmarkers of cancer classification and outcome prediction in a variety oftumor types.

4. Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a method that allows thesimultaneous and quantitative analysis of a large number of genetranscripts, without the need of providing an individual hybridizationprobe for each transcript. First, a short sequence tag (about 10-14 bp)is generated that contains sufficient information to uniquely identify atranscript, provided that the tag is obtained from a unique positionwithin each transcript. Then, many transcripts are linked together toform long serial molecules, that can be sequenced, revealing theidentity of the multiple tags simultaneously. The expression pattern ofany population of transcripts can be quantitatively evaluated bydetermining the abundance of individual tags, and identifying the genecorresponding to each tag. For more details see, e.g. Velculescu et al.,Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51(1997).

5. Gene Expression Analysis by Massively Parallel Signature Sequencing(MPSS)

This method, described by Brenner et al., Nature Biotechnology18:630-634 (2000), is a sequencing approach that combines non-gel-basedsignature sequencing with in vitro cloning of millions of templates onseparate 5 μm diameter microbeads. First, a microbead library of DNAtemplates is constructed by in vitro cloning. This is followed by theassembly of a planar array of the template-containing microbeads in aflow cell at a high density (typically greater than 3×10⁶microbeads/cm²). The free ends of the cloned templates on each microbeadare analyzed simultaneously, using a fluorescence-based signaturesequencing method that does not require DNA fragment separation. Thismethod has been shown to simultaneously and accurately provide, in asingle operation, hundreds of thousands of gene signature sequences froma yeast cDNA library.

6. Immunohistochemistry

Immunohistochemistry methods are also suitable for detecting theexpression levels of the prognostic markers of the present invention.Thus, antibodies or antisera, preferably polyclonal antisera, and mostpreferably monoclonal antibodies specific for each marker are used todetect expression. The antibodies can be detected by direct labeling ofthe antibodies themselves, for example, with rajlioactive labels,fluorescent labels, hapten labels such as, biotin, or an enzyme such ashorse radish peroxidase or alkaline phosphatase. Alternatively,unlabeled primary antibody is used in conjunction with a labeledsecondary antibody, comprising antisera, polyclonal antisera or amonoclonal antibody specific for the primary antibody.Immunohistochemistry protocols and kits are well known in the art andare commercially available.

7. Proteomics

The term “proteome” is defined as the totality of the proteins presentin a sample (e.g. tissue, organism, or cell culture) at a certain pointof time. Proteomics includes, among other things, study of the globalchanges of protein expression in a sample (also referred to as“expression proteomics”). Proteomics typically includes the followingsteps: (1) separation of individual proteins in a sample by 2-D gelelectrophoresis (2-D PAGE); (2) identification of the individualproteins recovered from the gel, e.g. my mass spectrometry or N-terminalsequencing, and (3) analysis of the data using bioinformatics.Proteomics methods are valuable supplements to other methods of geneexpression profiling, and can be used, alone or in combination withother methods, to detect the products of the prognostic markers of thepresent invention.

8. General Description of the mRNA Isolation, Purification andAmplification

The steps of a representative protocol for profiling gene expressionusing fixed, paraffin-embedded tissues as the RNA source, including mRNAisolation, purification, primer extension and amplification are providedin various published journal articles (for example: T. E. Godfrey etal., J. Molec. Diagnostics 2: 84-91 [2000]; K. Specht et al., Am. J.Pathol. 158: 419-29 [2001]). Briefly, a representative process startswith cutting about 10 μm thick sections of paraffin-embedded tumortissue samples. The RNA is then extracted, and protein and DNA areremoved. After analysis of the RNA concentration, RNA repair and/oramplification steps may be included, if necessary, and RNA is reversetranscribed using gene specific promoters followed by RT-PCR. Finally,the data are analyzed to identify the best treatment option(s) availableto the patient on the basis of the characteristic gene expressionpattern identified in the tumor sample examined, dependent on thepredicted likelihood of cancer recurrence.

9. Breast Cancer Gene Set, Assayed Gene Subsequences. and ClinicalApplication of Gene Expression Data

An important aspect of the present invention is to use the measuredexpression of certain genes by breast cancer tissue to provideprognostic information. For this purpose it is necessary to correct for(normalize away) both differences in the amount of RNA assayed andvariability in the quality of the RNA used. Therefore, the assaytypically measures and incorporates the expression of certainnormalizing genes, including well known housekeeping genes, such asGAPDH and Cyp1. Alternatively, normalization can be based on the mean ormedian signal (Ct) of all of the assayed genes or a large subset thereof(global normalization approach). On a gene-by-gene basis, measurednormalized amount of a patient tumor mRNA is compared to the amountfound in a breast cancer tissue reference set. The number (N) of breastcancer tissues in this reference set should be sufficiently high toensure that different reference sets (as a whole) behave essentially thesame way. If this condition is met, the identity of the individualbreast cancer tissues present in a particular set will have nosignificant impact on the relative amounts of the genes assayed.Usually, the breast cancer tissue reference set consists of at leastabout 30, preferably at least about 40 different FPE breast cancertissue specimens. Unless noted otherwise, normalized expression levelsfor each mRNA/tested tumor/patient will be expressed as a percentage ofthe expression level measured in the reference set. More specifically,the reference set of a sufficiently high number (e.g. 40) of tumorsyields a distribution of normalized levels of each mRNA species. Thelevel measured in a particular tumor sample to be analyzed falls at somepercentile within this range, which can be determined by methods wellknown in the art. Below, unless noted otherwise, reference to expressionlevels of a gene assume normalized expression relative to the referenceset although this is not always explicitly stated.

10. Design of Intron-Based PCR Primers and Probes

According to one aspect of the present invention, PCR primers and probesare designed based upon intron sequences present in the gene to beamplified. Accordingly, the first step in the primer/probe design is thedelineation of intron, sequences within the genes. This can be done bypublicly available software, such as the DNA BLAT software developed byKent, W. J., Genome Res. 12(4):656-64 (2002), or by the BLAST softwareincluding its variations. Subsequent steps follow well establishedmethods of PCR primer and probe design.

In order to avoid non-specific signals, it is important to maskrepetitive sequences within the introns when designing the primers andprobes. This can be easily accomplished by using the Repeat Maskerprogram available on-line through the Baylor College of Medicine, whichscreens DNA sequences against a library of repetitive elements andreturns a query sequence in which the repetitive elements are masked.The masked intron sequences can then be used to design primer and probesequences using any commercially or otherwise publicly availableprimer/probe design packages, such as Primer Express (AppliedBiosystems), MGB assay-by-design (Applied Biosystems); Primer3 (SteveRozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general usersand for biologist programmers. In: Krawetz S, Misener S (eds)Bioinformatics Methods and Protocols: Methods in Molecular Biology.Humana Press, Totowa, N.J., pp 365-386)

The most important factors considered in PCR primer design includeprimer length, melting temperature (Tm), and G/C content, specificity,complementary primer sequences, and 3′-end sequence. In general, optimalPCR primers are generally 17-30 bases in length, and contain about20-80%, such as, for example, about 50-60% G+C bases. Tm's between 50and 80° C., e.g. about 50 to 70° C. are typically preferred.

For further guidelines for PCR primer and probe design see, e.g.Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in:PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press,New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs”in: PCR Protocols, A Guide to Methods and Applications, CRC Press,London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer andprobe design. Methods Mol. Biol. 70:520-527 (1997), the entiredisclosures of which are hereby expressly incorporated by reference.

Further details of the invention will be described in the followingnon-limiting Example.

Example

A Phase II Study of Gene Expression in 242 Malignant Breast Tumors

A gene expression study was designed and conducted with the primary goalto molecularly characterize gene expression in paraffin-embedded, fixedtissue samples of invasive, breast ductal carcinoma, and to explore thecorrelation between such molecular profiles and disease-free survival.

Study Design

Molecular assays were performed on paraffin-embedded, formalin-fixedprimary breast tumor tissues obtained from 252 individual patientsdiagnosed with invasive breast cancer. All patients were lymphnode-negative, ER-positive, and treated with Tamoxifen. Mean age was 52years, and mean clinical tumor size was 2 cm. Median follow-up was 10.9years. As of Jan. 1, 2003, 41 patients had local or distant diseaserecurrence or breast cancer death. Patients were included in the studyonly if histopathologic assessment, performed as described in theMaterials and Methods section, indicated adequate amounts of tumortissue and homogeneous pathology.

Materials and Methods

Each representative tumor block was characterized by standardhistopathology for diagnosis, semi-quantitative assessment of amount oftumor, and tumor grade. When tumor area was less than 70% of thesection, the tumor area was grossly dissected and tissue was taken from6 (10 micron) sections. Otherwise, a total of 3 sections (also 10microns in thickness each) were prepared. Sections were placed in twoCostar Brand Microcentrifuge Tubes (Polypropylene, 1.7 mL tubes, clear).If more than one tumor block was obtained as part of the surgicalprocedure, the block most representative of the pathology was used foranalysis.

Gene Expression Analysis

mRNA was extracted and purified from fixed, paraffin-embedded tissuesamples, and prepared for gene expression analysis as described inchapter 6 above.

Molecular assays of quantitative gene expression were performed byRT-PCR, using the ABI PRISM 7900™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA). ABI PRISM7900™ consists of a thermocycler, laser, charge-coupled device (CCD),camera and computer. The system amplifies samples in a 384-well formaton a thermocycler. During amplification, laser-induced fluorescentsignal is collected in real-time through fiber optics cables for all 384wells, and detected at the CCD. The system includes software for runningthe instrument and for analyzing the data.

Analysis and Results

Tumor tissue was analyzed for 187 cancer-related genes and 5 referencegenes. Adequate RT-PCR profiles were obtained from 242 of the 252patients. The threshold cycle (CT) values for each patient werenormalized based on the median of the 7 reference genes for thatparticular patient. Clinical outcome data were available for allpatients from a review of registry data and selected patient charts.Outcomes were classified as:

Event: Alive with local, regional or distant breast cancer recurrence ordeath due to breast cancer.

No Event: Alive without local, regional or distant breast cancerrecurrence or alive with contralateral breast cancer recurrence or alivewith non-breast second primary cancer or died prior to breast cancerrecurrence.

Analysis was performed by:

A. determination of the relationship between normalized gene expressionand the binary outcomes of 0 or 1;

B. Analysis of the relationship between normalized gene expression andthe time to outcome (0 or 1 as defined above) where patients who werealive without breast cancer recurrence or who died due to a cause otherthan breast cancer were censored. This approach was used to evaluate theprognostic impact of individual genes and also sets of multiple genes.

Analysis of Patients with Invasive Breast Carcinoma by Binary Approach

In the first (binary) approach, analysis was performed on all 242patients with invasive breast carcinoma. A t test was performed on thegroups of patients classified as either no recurrence and no breastcancer related death at 10 years, versus recurrence, or breastcancer-related death at 10 years, and the p-values for the differencesbetween the groups for each gene were calculated.

Table 1 lists the 33 genes for which the p-value for the differencesbetween the groups was <0.05. The first column of mean expression valuespertains to patients who had a metastatic recurrence of nor died frombreast cancer. The second column of mean expression values pertains topatients who neither had a metastatic recurrence of nor died from breastcancer.

TABLE 1 Mean Mean group B group A No T Gene Event event statistic Pvalue MMP9 −3.15 −4.27 3.75 0.00 GSTM1 −5.02 −4.03 −3.56 0.00 MELK −3.89−4.66 3.34 0.00 PR −4.56 −3.18 −3.27 0.00 DKFZp586M07 −3.83 −2.94 −3.090.00 GSTM3 −2.56 −1.69 −3.06 0.00 MCM2 −3.51 −4.08 3.03 0.00 CDC20 −3.01−3.75 3.01 0.00 CCNB1 −4.48 −5.17 3.02 0.00 STMY3 −0.58 −1.20 2.95 0.00GRB7 −1.93 −3.01 2.98 0.00 MYBL2 −3.91 −4.78 2.91 0.01 CEGP1 −3.00 −1.85−2.89 0.01 SURV −4.23 −5.06 2.88 0.01 LMNB1 −2.40 −2.91 2.81 0.01 CTSL2−5.74 −6.39 2.83 0.01 PTTG1 −3.49 −4.14 2.72 0.01 BAG1 −1.76 −1.30 −2.580.01 KNSL2 −3.35 −4.06 2.60 0.01 CIAP1 −4.44 −4.02 −2.58 0.01 PREP −3.34−3.74 2.56 0.01 NEK2 −5.25 −5.80 2.53 0.01 EpCAM −1.95 −2.31 2.50 0.01PCNA −2.79 −3.13 2.42 0.02 C20_orf1 −2.48 −3.09 2.39 0.02 ITGA7 −4.53−3.87 −2.37 0.02 ID1 −2.58 −2.17 −2.30 0.02 B_Catenin −1.32 −1.08 −2.280.03 EstR1 −0.78 −0.12 −2.28 0.03 CDH1 −2.76 −3.27 2.20 0.03 TS −2.86−3.29 2.18 0.03 HER2 0.53 −0.22 2.18 0.03 cMYC −3.22 −2.85 −2.16 0.04

In the foregoing Table 1, negative t-values indicate higher expression,associated with better outcomes, and, inversely, higher (positive)t-values indicate higher expression associated with worse outcomes.Thus, for example, elevated expression of the CCNB1 gene (t-value=3.02;CT mean alive<CT mean deceased) indicates a reduced likelihood ofdisease free survival. Similarly, elevated expression of the GSTM1 gene(t-value=−3.56; CT mean alive>CT mean deceased) indicates an increasedlikelihood of disease free survival.

Thus, based on the data set forth in Table 1, the expression of any ofthe following genes in breast cancer indicates a reduced likelihood ofsurvival without cancer recurrence: C20_orf1; CCNB1; CDC20; CDH1; CTSL2;EpCAM; GRB7; HER2; KNSL2; LMNB; MCM2; MMP9; MYBL2; NEK2; PCNA; PREP;PTTG1; STMY3; SURV; TS; MELK

Based on the data set forth in Table 1, the expression of any of thefollowing genes in breast cancer indicates a better prognosis forsurvival without cancer recurrence: BAG1; BCatenin; CEGP1; CIAP1; cMYC;DKFZp586M07; EstR1; GSTM1; GSTM3; ID1; ITGA7; PR.

Analysis of Multiple Genes and Indicators of Outcome

Two approaches were taken in order to determine whether using multiplegenes would provide better discrimination between outcomes. First, adiscrimination analysis was performed using a forward stepwise approach.Models were generated that classified outcome with greaterdiscrimination than was obtained with any single gene alone. Accordingto a second approach (time-to-event approach), for each gene a CoxProportional Hazards model (see, e.g. Cox, D. R., and Oakes, D. (1984),Analysis of Survival Data, Chapman and Hall, London, New York) wasdefined with time to recurrence or death as the dependent variable, andthe expression level of the gene as the independent variable. The genesthat hive a p-value <0.05 in the Cox model were identified. For eachgene, the Cox model provides the relative risk (RR) of recurrence ordeath for a unit change in the expression of the gene. One can choose topartition the patients into subgroups at any threshold value of themeasured expression (on the CT scale), where all patients withexpression values above the threshold have higher risk, and all patientswith expression values below the threshold have lower risk, or viceversa, depending on whether the gene is an indicator of bad (RR>1.01) orgood (RR<1.01) prognosis. Thus, any threshold value will definesubgroups of patients with respectively increased or decreased risk. Theresults are summarized in Table 2, which lists the 42 genes for whichthe p-value for the differences between the groups was <0.05.

TABLE 2 Gene Relative Risk p-value GRB7 1.52 0.000011 SURV 1.57 0.000090PR 0.74 0.000129 LMNB1 1.92 0.000227 MYBL2 1.46 0.000264 HER2 1.460.000505 GSTM1 0.68 0.000543 MELK 1.59 0.000684 C20_orf1 1.59 0.000735PTTG1 1.63 0.001135 BUB1 1.58 0.001425 CDC20 1.54 0.001443 CCNB1 1.600.001975 STMY3 1.47 0.002337 KNSL2 1.48 0.002910 CTSL2 1.43 0.003877MCM2 1.59 0.005203 NEKS 1.48 0.006533 DR5 0.62 0.006660 Ki_67 1.460.008188 CCNE2 1.38 0.009505 TOP2A 1.38 0.009551 PCNA 1.67 0.010237 PREP1.69 0.012308 FOXM1 1.52 0.012837 NME1 1.46 0.013622 CEGP1 0.84 0.013754BAG1 0.68 0.015422 STK15 1.46 0.017013 HNRPAB 1.96 0.017942 EstR1 0.800.018877 MMP9 1.19 0.019591 DKFZp586M07 0.79 0.020073 TS 1.44 0.025186Src 1.70 0.037398 BIN1 0.75 0.038979 NPD009 0.80 0.039020 RPLPO 0.520.041575 GSTM3 0.84 0.041848 MMP12 1.27 0.042074 TFRC 1.57 0.046145IGF1R 0.78 0.046745

Based on the data set forth in Table 2, the expression of any of thefollowing genes in breast cancer indicates a reduced likelihood ofsurvival without cancer recurrence: GRB7; SURV; LMNB; MYBL2; HER2; MELK;C20_orf1; PTTG1; BUB1; CDC20; CCNB1; STMY3; KNSL2; CTSL2; MCM2; NEK2;Ki_67; CCNE2; TOP2A-4; PCNA; PREP; FOXM1; NME1; STKI5; HNRPAB; MMP9; TS;Src; MMPI2; TFRC.

Based on the data set forth in Table 2, the expression of any of thefollowing genes in breast cancer indicates a better prognosis forsurvival without cancer recurrence: PR; GSTM1; DR5; CEGP1; BAG1; EstR1;DKFZp586MO7; BIN1; NPD009; RPLPO; GSTM3; IGFIR.

The binary and time-to-event analyses, with few exceptions, identifiedthe same genes as prognostic markers. For example, comparison of Tables1 and 2 shows that 10 genes were represented in the top 15 genes in bothlists. Furthermore, when both analyses identified the same gene at[p<0.10], which happened for 26 genes, they were always 1 concordantwith respect to the direction (positive or negative sign) of thecorrelation with survival/recurrence. Overall, these results strengthenthe conclusion that the identified markers have significant prognosticvalue.

Multivariate Gene Analysis of 242 Patients with Invasive BreastCarcinoma

For Cox models comprising more than two genes (multivariate models),stepwise entry of each individual gene into the model is performed,where the first gene entered is pre-selected from among those geneshaving significant univariate p-values, and the gene selected for entryinto the model at each subsequent step is the gene that best improvesthe fit of the model to the data. This analysis can be performed withany total number of genes. In the analysis the results of which areshown below, stepwise entry was performed for up to 10 genes.

Multivariate analysis was performed using the following equation:

RR=exp[coef(geneA)×Ct(geneA)+coef(geneB)×Ct(geneB)+coef(geneC)×Ct(geneC)+. . . ].

In this equation, coefficients for genes that are predictors ofbeneficial outcome are positive numbers and coefficients for genes thatare predictors of unfavorable outcome are negative numbers. The “Ct”values in the equation are ACts, i.e. reflect the difference between theaverage normalized Ct value for a population and the normalized Ctmeasured for the patient in question. The convention used in the presentanalysis has been that ACts below and above the population average havepositive signs and negative signs, respectively (reflecting greater orlesser mRNA abundance). The relative risk (RR) calculated by solvingthis equation will indicate if the patient has an enhanced or reducedchance of long-term survival without cancer recurrence.

A multivariate stepwise analysis, using the Cox Proportional HazardsModel, was performed on the gene expression data obtained for all 242patients with invasive breast carcinoma. The following ten-gene set hasbeen identified by this analysis as having particularly strongpredictive value of patient survival: GRB7; LMNB1; ER; STMY3; KLK10; PR;KRT5; FGFR1; MCM6; SNRPF. In this gene set ER, PR, KRT5 and MCM6contribute to good prognosis, while GRB7, LMNB1, STMY3, KLK10, FGFR1,and SNRPF contribute to poor prognosis.

While the present invention has been described with reference to whatare considered to be the specific embodiments; it is to be understoodthat the invention is not limited to Fuch embodiments. To the contrary,the invention is intended to cover various modifications and equivalentsincluded within the spirit and scope of the appended claims. Forexample, while the disclosure focuses on the identification of variousbreast cancer associated genes and gene sets, and on the personalizedprognosis of breast cancer, similar genes, gene sets and methodsconcerning other types of cancer are specifically within the scopeherein. In particular, the present gene sets or variants thereof can beused as prognostic markers to predict the likelihood of long-termsurvival or cancer recurrence in the case of ovarian cancer.

All references cited throughout the disclosure are hereby expresslyincorporated by reference.

TABLE 3 Gene Accession Start Stop SEQ ID NO. Sequence B-CateninNM_001904 1549 1629 SEQ ID NO: 1GGCTCTTGTGCGTACTGTCCTTCGGGCTGGTGACAGGGAAGACATCACTGAGCCTGCCATCTGTGCTCTTCGTCATCTGABAG1 NM_004323 673 754 SEQ ID NO: 2CGTTGTCAGCACTTGGAATACAAGATGGTTGCCGGGTCATGTTAATTGGGAAAAAGAACAGTCCACAGGAAGAGGTTGAACBIN1 NM_004305 866 942 SEQ ID NO: 3CCTGCAAAAGGGAACAAGAGCCCTTCGCCTCCAGATGGCTCCCCTGCCGCCACCCCCGAGATCAGAGTCAACCACGBUB1 NM_004338 1002 1070 SEQ ID NO: 4CCGAGGTTAATCCAGCACGTATGGGGCCAAGTGTAGGCTCCCAGCAGGAACTGAGAGCGCCATGTCTTC20 orf1 NM_012112 2675 2740 SEQ ID NO: 5TCAGCTGTGAGCTGCGGATACCGCCCGGCAATGGGACCTGCTCTTAACCTCAAACCTAGGACCGT CCNB1NM_031966 823 907 SEQ ID NO: 6TTCAGGTTGTTGCAGGAGACCATGTACATGACTGTCTCCATTATTGATCGGTTCATGCAGAATAATTGTGTGCCCAAGAAGATGCCNE2 NM_057749 2026 2108 SEQ ID NO: 7ATGCTGTGGCTCCTTCCTAACTGGGGCTTTCTTGACATGTAGGTTGCTTGGTAATAACCTTTTTGTATATCACAATTTGGGTCDC20 NM_001255 679 747 SEQ ID NO: 8TGGATTGGAGTTCTGGGAATGTACTGGCCGTGGCACTGGACAACAGTGTGTACCTGTGGAGTGCAAGCCDH1 NM_004360 2499 2580 SEQ ID NO: 9TGAGTGTCCCCCGGTATCTTCCCCGCCCTGCCAATCCCGATGAAATTGGAAATTTTATTGATGAAAATCTGAAAGCGGCTGCEGP1 NM_020974 563 640 SEQ ID NO: 10TGACAATCAGCACACCTGCATTCACCGCTCGGAAGAGGGCCTGAGCTGCATGAATAAGGATCACGGCTGTAGTGACACIAP1 MM 001166 1822 1894 3E0 ID NO: 11TGCCTGTGGTGGGAAGCTCAGTAACTGGGAACCAAAGGATGATGCTATGTCAGAACACCGGAGGCATTTTCCdMYC NM_002467 1494 1578 SEQ ID NO: 12TCCCTCCACTCGGAAGGACTATCCTGCTGCCAAGAGGGTCAAGTTGGACAGTGTCAGAGTCCTGAGACAGATCAGCAACAACCGCTSL2 NM_001333 671 738 SEQ ID NO: 13TGTCTCACTGAGCGAGCAGAATCTGGTGGACTGTTCGCGTCCTCAAGGCAATCAGGGCTGCAATGGTDKFZp566 AL050227 559 633 SEQ ID NO: 14TCCATTTTCTACCTGTTAACCTTCATCATTTTGTGCAGGCCCTGGAAGCAAAGAGAGGAAGGGACCGACTGCATDR5 NM_003842 1127 1211 SEQ ID NO: 15CTCTGAGACAGTGCTTCGATGACTTTGCAGACTTGGTGCCCTTTGACTCCTGGGAGCCGCTCATGAGGAAGTTGGGCCTCATGGEpCAM NM_002354 435 510 SEQ ID NO: 16GGGCCCTCCAGAACAATGATGGGCTTTATGATCCTGACTGCGATGAGAGCGGGCTCTTTAAGGCCAAGCAGTGCAEstR1 NM_000125 1956 2024 SEQ ID NO: 17CGTGGTGCCCCTCTATGACCTGCTGCTGGAGATGCTGGACGCCCACCGCCTACATGCGCCCACTAGCCFGFR1 MM 023109 2685 2759 SEQ ID NO: 18CACGGGACATTCACCACATCGACTACTATAAAAAGACAACCAACGGCCGACTGCCTGTGAAGTGGATGGCACCCFOXM1 NM_021953 1898 1980 SEQ ID NO: 19CCACCCCGAGCAAATCTGTCCTCCCCAGAACCCCTGAATCCTGGAGGCTCACGCCCCCAGCCAAAGTAGGGGGACTGGATTTGRB7 NM_005310 1275 1342 SEQ ID NO: 20CCATCTGCATCCATCTTGTTTGGGCTCCCCACCCTTGAGAAGTGCCTCAGATAATACCCTGGTGGCCGSTM1 NM_000551 93 179 SEQ ID NO: 21AAGCTATGAGGAAAAGAAGTACACGATGGGGGACGCTCCTGATTATGACAGAAGCCAGTGGCTGAATGAAAAATTCAAGCTGGGCCGSTM3 NM_000849 248 324 SEQ ID NO: 22CAATGCCATCTTGCGCTACATCGCTCGCAAGCACAACATGTGTGGTGAGACTGAAGAAGAAAAGATTCGAGTGGACHER2 NM_004443 1138 1208 SEQ ID NO: 23CGGTGTGAGAAGTGCAGCAAGCCCTGTGCCCGAGTGTGCTATGGTCTGGGCATGGAGCACTTGCGAGAGGHNRPAB NM_004199 1086 1170 SEQ ID NO: 24CAAGGGAGCGACCAACTGATCGCACACATGCTTTGTTTGGATATGGAGTGAACACAATTATGTACCAAATTTAACTTGGCAAACID1 NM_002165 286 356 SEQ ID NO: 25AGAACCGCAAGGTGAGCAAGGTGGAGATTCTCCAGCACGTCATCGACTACATCAGGGACCTTCAGTTGGAIGF1R NM_000875 3467 3550 SEQ ID NO: 26GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGATTTTGGTATGACGCGAGATATCTATGAGACAGACTATTACCGGAAAITGA7 NM_002206 633 712 SEQ ID NO: 27GATATGATTGGTCGCTGCTTTGTGCTCAGCCAGGACCTGGCCATCCGGGATGAGTTGGATGGTGGGGAATGGAAGTTCTKI-67 NM_002417 42 122 SEQ ID NO: 28CGGACTTTGGGTGCGACTTGACGAGCGGTGGTTCGACAAGTGGCCTTGCGGGCCGGATCGTCCCAGTGGAAGAGTTGTAAKLK10 NM_002776 966 1044 SEQ ID NO: 29GCCCAGAGGCTCCATCGTCCATCCTCTTCCTCCCCAGTCGGCTGAACTCTCCCCTTGTCTGCACTGTTCAAACCTCTGKNSL2 BC000712 1266 1343 SEQ ID NO: 30CCACCTCGCCATGATTTTTCCTTTGACCGGGTATTCCCACCAGGAAGTGGACAGGATGAAGTGTTTGAAGAGATTGCKRT5 NM_000424 1605 1674 SEQ ID NO: 31TCAGTGGAGAAGGAGTTGGACCAGTCAACATCTCTGTTGTCACAAGCAGTGTTTCCTCTGGATATGGCALMNB1 NM_005573 1500 1566 SEQ ID NO: 32TGCAAACGCTGGTGTCACAGCCAGCCCCCCAACTGACCTCATCTGGAAGAACCAGAACTCGTGGGG MCM2MM 004526 2442 2517 SEQ ID NO: 33GACTTTTGCCCGCTACCTTTCATTCCGGCGTGACAACAATGAGCTGTTGCTCTTCATACTGAAGCAGTTAGTGGCMCM6 NM_005915 2669 2751 SEQ ID NO: 34TGATGGTCCTATGTGTCACATTCATCACAGGTTTCATACCAACACAGGCTTCAGCACTTCCTTTGGTGTGTTTCCTGTCCCAMELK MM 014791 22 87 SEQ ID NO: 35AACCCGGCGATCGAAAAGATTCTTAGGAACGCCGTACCAGCCGCGTCTCTCAGGACAGCAGGCCC MMP12NM_002426 816 894 5E0 ID NO: 36CCAACGCTTGCCAAATCCTGACAATTCAGAACCAGCTCTCTGTGACCCCAATTTGAGTTTTGATGCTGTCACTACCGTMMP9 NM_004994 124 191 SEQ ID NO: 37GAGAACCAATCTCACCGACAGGCAGCTGGCAGAGGAATACCTGTACCGCTATGGTTACACTCGGGTGMYBL2 NM_002466 599 673 SEQ ID NO: 38GCCGAGATCGCCAAGATGTTGCCAGGGAGGACAGACAATGCTGTGAAGAATCACTGGAACTCTACCATCAAAAGNEK2 NM_032497 102 161 SEQ ID NO: 39GTGAGGCAGCGCGACTCTGGCGACTGGCCGGCCATGCCTTCCCGGGCTGAGGACTATGAAGTGTTGTACACCATTGGCANME1 NM_000259 365 439 SEQ ID NO: 40CCAACCCTGCACACTCCAAGCCTGGGACCATCCGTGGAGACTTCTGCATACAAGTTGGCAGGAACATTATACATNPD009 NM_020686 589 662 SEQ ID NO: 41GGCTGTGGCTGAGGCTGTAGCATCTCTGCTGGAGGTGAGACACTCTGGGAACTGATTTGACCTCGAATGCTCCPCNA NM_092592 157 228 SEQ ID NO: 42GAAGGTGTTGGAGGCACTCAAGGACCTCATCAACGAGGCCTGCTGGGATATTAGCTCCAGCGGTGTAAACCPR NM_000926 1895 1980 SEQ ID NO: 43GCATCAGGCTGTCATTATGGTGTCCTTACCTGTGGGAGCTGTAAGGTCTTCTTTAAGAGGGCAATGGAAGGGCAGCACAACTACTPREP NM_002726 889 965 SEQ ID NO: 44GGGACGGTGTTCACATTCAAGACGAATCGCCAGTCTCCCAACTATCGCGTGATCAACATTGACTTCTGGGATCCTGPTTG1 NM_004219 48 122 SEQ ID NO: 45GGCTACTCTGATCTATGTTGATAAGGAAAATGGAGAACCAGGCACCCGTGTGGTTGCTAAGGATGGGCTGAAGCRPLPO NM_001002 791 866 SEQ ID NO: 46CCATTCTATCATCAACGGGTACAAACGAGTCCTGGCCTTGTCTGTGGAGACGGATTACACCTTCCCACTTGCTGASNRPF NM_003095 71 150 SEQ ID NO: 47GGCTGGTCGGCAGAGAGTAGCCTGCAACATTCGGCCGTGGTTTACATGAGTTTACCCCTCAATCCCAAACCTTTCCTCASrc NM_004383 979 1043 SEQ ID NO: 48CCTGAACATGAAGGAGCTGAAGCTGCTGCAGACCATCGGGAAGGGGGAGTTCGGAGACGTGATG STK15NM_003600 1101 1170 SEQ ID NO: 49CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGGASTMY3 NM_005940 2990 2180 SEQ ID NO: 50CCTGGAGGCTGCAACATACCTCAATCCTGTCCCAGGGCGGATCCTCCTGAAGCCCTTTTCGCAGCACTGCTATCCTCCAAAGCCATTGTASURV NM_001188 737 817 SEQ ID NO: 51TGTTTTGATTCCCGGGCTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTCCCTTTTGCTAGAGCTGACAGCTTGTFRC NM_003234 2110 2178 SEQ ID NO: 52GCCAACTGCTTTCATTTGTGAGGGATCTGAACCAATACAGAGCAGACATAAAGGAAATGGGCCTGAGTTOP2A NM_001087 4505 4577 SEQ ID NO: 53AATCCAAGGGGGACAGTGATGACTTCCATATGGACTTTGACTCAGCTGTGGCTCCTCGGGCAAAATCTGTACYS NM_001071 784 629 SEQ ID NO: 54GCCTCGGTGTGCCTTTCAACATCGCCAGCTACGCCCTGCTCACGTACATGATTGCGCACATCACG

TABLE 4A Gene Acceesion Name SEQ ID NO Sequence B-Catenin NM_001904S2150/B-Cate.f3 SEQ ID NO: 55 GGCTCTTGTGCGTACTGTCCTT 22 B-CateninNM_001904 S2151/B-Cate.r3 SEQ ID NO: 56 TCAGATGACGAAGAGCACAGATG 23B-Catenin NM_001904 S5046/B-Cate.p3 SEQ ID NO: 57AGGCTCAGTGATGTCTTCCCTGTCACCAG 29 BAG1 NM_004323 S1386/BAG1.f2SEQ ID NO: 58 CGTTGTCAGCACTTGGAATACAA 23 BAG1 NM_004323 S1387/BAG1.r2SEQ ID NO: 59 GTTCAACCTCTTCCTGTGGACTGT 24 BAG1 NM_004323 S4731/BAG1.p2SEQ ID NO: 60 CCCAATTAACATGACCCGGCAACCAT 26 BIN1 NM_004305 S2651/BIN1.f3SEQ ID NO: 61 CCTGCAAAAGGGAACAAGAG 20 BIN1 NM_004305 S2652/BIN1.r3SEQ ID NO: 62 CGTGGTTGACTCTGATCTCG 20 BIN1 NM_004305 S4954/BIN1.p3SEQ ID NO: 63 CTTCGCCTCCAGATGGCTCCC 21 BUB1 NM_004336 S4294/BUB1.f1SEQ ID NO: 64 CCGAGGTTAATCCAGCACGTA 21 BUB1 NM_004336 S4295/BUB1.r1SEQ ID NO: 65 AAGACATGGCGCTCTCAGTTC 21 BUB1 NM_004336 S4296/BUB1.p1SEQ ID NO: 66 TGCTGGGAGCCTACACTTGGCCC 23 C20 orf1 NM_012112S3560/C20 or.f1 SEQ ID NO: 67 TCAGCTGTGAGCTGCGGATA 20 C20 orf1 NM_012112S3561/C20 or.r1 SEQ ID NO: 68 ACGGTCCTAGGTCTGAGGTTAAGA 24 C20 orflNM_012112 S3562/C20 or.p1 SEQ ID NO: 69 CAGGTCCCATTGCCGGGCG 19 CCNB1NM_031966 S1720/CCNB1.f2 SEQ ID NO: 70 TTCAGGTTGTTGCAGGAGAC 20 CCNB1NM_031966 S1721/CCNB1.r2 SEQ ID NO: 71 CATCTTCTTGGGCACACAAT 20 CCNB1NM_031966 S4733/CCNB1.p2 SEQ ID NO: 72 TGTCTCCATTATTGATCGGTTCATGCA 27CCNE2 NM_057749 S1458/CCNE2.f2 SEQ ID NO: 73 ATGCTGTGGCTCCTTCCTAACT 22CCNE2 NM_057749 S1459/CCNE1.r2 SEQ ID NO: 74 ACCCAAATTGTGATATACAAAAAGGTT27 CCNE2 NM_057749 S4945/CCNE2.p2 SEQ ID NO: 75TACCAAGCAACCTACATGTCAAGAAAGCCC 30 CDC20 NM_001255 S4447/CDC20.f1SEQ ID NO: 76 TGGATTGGAGTTCTGGGAATG 21 CDC20 NM_001255 S4448/CDC20.r1SEQ ID NO: 77 GCTTGCACTCCACAGGTACACA 22 CDC20 NM_001255 S4449/CDC20.p1SEQ ID NO: 78 ACTGGCCGTGGCACTGGACAACA 23 CDH1 NM_004360 S0073/CDH1.f3SEQ ID NO: 79 TGAGTGTCCCCCGGTATCTTC 21 CDH1 NM_004360 S0075/CDH1.r3SEQ ID NO: 80 CAGCCGCTTTCAGATTTTCAT 21 CDH1 NM_004360 S4990/CDH1.p3SEQ ID NO: 81 TGCCAATCCCGATGAAATTGGAAATTT 27 CEGP1 NM_020974S1494/CEGP1.f2 SEQ ID NO: 82 TGACAATCAGCACACCTGCAT 21 CEGP1 NM_020974S1495/GEGP1.r2 SEQ ID NO: 83 TGTGACTACAGCCGTGATCCTTA 23 CEGP1 NM_020974S4735/CEGP1.p2 SEQ ID NO: 84 CAGGCCCTCTTCCGAGCGGT 20 CIAP1 NM_001166S0764/CIAP1.f2 SEQ ID NO: 85 TGCCTGTGGTGGGAAGCT 18 CIAP1 NM_001166S0765/CIAP1.r2 SEQ ID NO: 86 GGAAAATGCCTCCGGTGTT 19 CIAP1 NM_001166S4802/CIAP1.p2 SEQ ID NO: 87 TGACATAGCATCATCCTTTGGTTCCCAGTT 30 cMYCNM_002467 S0085/cMYC.f3 SEQ ID NO: 88 TCCCTCCACTCGGAAGGACTA 21 cMYCNM_002467 S0087/cMYC.r3 SEQ ID NO: 89 CGGTTGTTGCTGATCTGTCTCA 22 cMYCNM_002467 S4994/cMYC.p3 SEQ ID NO: 90 TCTGACACTGTCCAACTTGACCCTCTT 27CTSL2 NM_001333 S4354/CTSL2.f1 SEQ ID NO: 91 TGTCTCACTGAGCGAGCAGAA 21CTSL2 NM_001333 S4355/CTSL2.r1 SEQ ID NO: 92 ACCATTGCAGCCCTGATTG 19CTSL2 NM_001333 S4356/CTSL2.p1 SEQ ID NO: 93 CTTGAGGACGCGAACAGTCCACCA 24DKFZp586M0723 AL050227 S4396/DKFZp5.f1 SEQ ID NO: 94TCCATTTTCTACCTGTTAACCTTCATC 27 DKFZp586M0723 AL050227 S4397/DKF2p5.r1SEQ ID NO: 95 ATGCAGTCGGTCCCTTCCT 19 DKFZp586M0723 AL050227S4398/DKFZpS.p1 SEQ ID NO: 96 TTGCTTCCAGGGCCTGCACAAAA 23 DR5 NM_003842S2551/DR5.f2 SEQ ID NO: 97 CTCTGAGACAGTGCTTCGATGACT 24 DR5 NM_003842S2552/DR5.r2 SEQ ID NO: 98 CCATGAGGCCCAACTTCCT 19 DR5 NM_003842S4979/DR5.p2 SEQ ID NO: 99 CAGACTTGGTGCCCTTTGACTCC 23 EpCAM NM_002354S1807/EpCAM.f1 SEQ ID NO: 100 GGGCCCTCCAGAACAATGAT 20

TABLE 4B EpCAM NM_002354 S1808/EpCAM.r1 SEQ ID NO 101TGCACTGCTTGGCCTTAAAGA 21 EpCAM NM_002354 S4984/EpCAM.p1 SEQ ID NO: 102CCGCTCTCATCGCAGTCAGGATCAT 25 EstR1 NM_000125 S0115/EstR1.f1SEQ ID NO: 103 CGTGGTGCCCCTCTATGAC 19 EstR1 NM_000125 S0117/EstR1.r1SEQ ID NO: 104 GGCTAGTGGGCGCATGTAG 19 EstR1 NM_000125 S4737/EstR1.p1SEQ ID NO: 105 CTGGAGATGCTGGACGCCC 19 FGFR1 NM_023109 S0818/FGFR1.f3SEQ ID NO: 106 CACGGGACATTCACCACATC 20 FGFR1 NM_023109 S0819/FGFR1.r3SEQ ID NO: 107 GGGTGCCATCCACTTCACA 19 FGFR1 NM_023109 S4816/FGFR1.p3SEQ ID NO: 108 ATAAAAAGACAACCAACGGCCGACTGC 27 FOXM1 NM_021953S2006/FOXM1.f1 SEQ ID NO: 109 CCACCCCGAGCAAATCTGT 19 FOXM1 NM_021953S2007/FOXM1.r1 SEQ ID NO: 110 AAATCCAGTCCCCCTACTTTGG 22 FOXM1 NM_021953S4757/FOXM1.p1 SEQ ID NO: 111 CCTGAATCCTGGAGGCTCACGCC 23 GRB7 NM_005310S0130/GRB7.f2 SEQ ID NO: 112 ccatctgcatccatcftgft 20 GRB7 NM_005310S0132/GRB7.r2 SEQ ID NO: 113 ggccaccagggtattatctg 20 GRB7 NM_005310S4726/GRB7.p2 SEQ ID NO: 114 ctccccacccttgagaagtgcct 23 GSTM1 NM_000561S2026/GSTM1.r1 SEQ ID NO: 115 GGCCCAGCTTGAATTTTTCA 20 GSTM1 NM_000561S2027/GSTM1.f1 SEQ ID NO: 116 AAGCTATGAGGAAAAGAAGTACACGAT 27 GSTM1NM_000561 S4739/GSTMl.p1 SEQ ID NO: 117 TCAGCCACTGGCTTCTGTCATAATCAGGAG30 GSTM3 NM_000849 S2038/GSTM3.f2 SEQ ID NO: 118 CAATGCCATCTTGCGCTACAT21 GSTM3 NM_000849 S2039/GSTM3.r2 SEQ ID NO: 119GTCCACTCGAATCTTTTCTTCTTCA 25 GSTM3 NM_000849 S5064/GSTM3.p2SEQ ID NO: 120 CTCGCAAGCACAACATGTGTGGTGAGA 27 HER2 NM_004448S0142/HER2.f3 SEQ ID NO: 121 CGGTGTGAGAAGTGCAGCAA 20 HER2 NM_004448S0144/HER2.r3 SEQ ID NO: 122 CCTCTCGCAAGTGCTCCAT 19 HER2 NM_004448S4729/HER2.p3 SEQ ID NO: 123 CCAGACCATAGCACACTCGGGCAC 24 HNRPABNM_004499 S4510/HNRPAB.f3 SEQ ID NO: 124 CAAGGGAGCGACCAACTGA 19 HNRPABNM_004499 S4511/HNRPAB.r3 SEQ ID NO: 125 GTTTGCCAAGTTAAATTTGGTACATAAT 28HNRPAB NM_004499 S4512/HNRPAB.p3 SEQ ID NO: 126CTCCATATCCAAACAAAGCATGTGTGCG 28 ID1 NM_002165 S0620/ID1.f1SEQ ID NO: 127 AGAACCGCAAGGTGAGCAA 19 ID1 NM_002165 S0821/ID1.r1SEQ ID NO: 128 TCCAACTGAAGGTCCCTGATG 21 ID1 NM_002165 S4832/ID1.p1SEQ ID NO: 129 TGGAGATTCTCCAGCACGTCATCGAC 26 IGF1R NM_000875S1249/IGF1R.f3 SEQ ID NO: 130 GCATGGTAGCCGAAGATTTCA 21 IGF1R NM_000875S1250/IGF1R.r3 SEQ ID NO: 131 TTTCCGGTAATAGTCTGTCTCATAGATATC 30 IGF1RNM_000875 S4895/IGF1R.p3 SEQ ID NO: 132 CGCGTCATACCAAAATCTCCGATTTTGA 28ITGA7 NM_002206 S0859/ITGA7.f1 SEQ ID NO: 133 GATATGATTGGTCGCTGCTTTG 22ITGA7 NM_002206 S0920/17GA7.r1 SEQ ID NO: 134 AGAACTTCCATTCCCCACCAT 21ITGA7 NM_002206 S4795/ITGA7.p1 SEQ ID NO: 135 CAGCCAGGACCTGGCCATCCG 21Ki-67 NM_002417 S0436/Ki-67.f2 SEQ ID NO: 136 CGGACTTTGGGTGCGACTT 19Ki-67 NM_002417 S0437/Ki-67.r2 SEQ ID NO: 137 TTACAACTCTTCCACTGGGACGAT24 Ki-67 NM_002417 S4741/K1-67.p2 SEQ ID NO: 138 CCACTTGTCGAACCACCGCTCGT23 KLK10 NM_002776 S2624/KLK10.f3 SEQ ID NO: 139 GCCCAGAGGCTCCATCGT 18KLK10 NM_002776 S2625/KLK10.r3 SEQ ID NO: 140 CAGAGGTTTGAACAGTGCAGACA 23KLK10 NM_002776 S4978/KLK10.p3 SEQ ID NO: 141 CCTCTTCCTCCCCAGTCGGCTGA 23KNSL2 BC000712 S4432/KNSL2.f2 SEQ ID NO: 142 CCACCTCGCCATGATTTTTC 20KNSL2 BC000712 S4433/KNSL2.r2 SEQ ID NO: 143 GCAATCTCTTCAAACACTTCATCCT25 KNSL2 BC000712 S4434/KNSL2.p2 SEQ ID NO: 144TTTGACCGGGTATTCCCACCAGGAA 25 KRT5 NM_000424 S0175/KRT5.f3 SEQ ID NO: 145tcagtggagaaggagttgga 20 KRT5 NM_000424 S0177/KRT5.r3 SEQ ID NO: 146tgccatatccagaggaaaca 20 KRT5 NM_000424 S5015/KRT5.p3 SEQ ID NO: 147ccagtcaacatctctgttgtcacaagca 28 LMNB1 NM_005573 S4477/LMNB1.f1SEQ ID NO: 148 TGCAAACGCTGGTGTCACA 19

TABLE 4C LMNB1 NM_005573 S4478/LMNB1.r1 SEQ ID NO: 149CCCCACGAGTTCTGGTTCTTC 21 LMNB1 NM_005573 S4479/LMNB1.p1 SEQ ID NO: 150CAGCCCCCCAACTGACCTCATC 22 MCM2 NM_004526 S1602/MCM2.f2 SEQ ID NO: 151GACTTTTGCCCGCTACCTTTC 21 MCM2 NM_004526 S1603/MCM2.r2 SEQ ID NO: 152GCCACTAACTGCTTCAGTATGAAGAG 26 MCM2 NM_004526 S4900/MCM2.p2SEQ ID NO: 153 ACAGCTCATTGTTGTCACGCCGGA 24 MCM6 NM_005915 S1704/MCM6.f3SEQ ID NO: 154 TGATGGTCCTATGTGTCACATTCA 24 MCM6 NM_005915 S1705/MCM6.r3SEQ ID NO: 155 TGGGACAGGAAACACACCAA 20 MCM6 NM_005915 S4919/MCM6.p3SEQ ID NO: 156 CAGGTTTCATACCAACACAGGCTTCAGCAC 30 MELK NM_014791S4318/MELK.f1 SEQ ID NO: 157 AACCCGGCGATCGAAAAG 18 MELK NM_014791S4319/MELK.r1 SEQ ID NO: 158 GGGCCTGCTGTCCTGAGA 18 MELK NM_014791S4320/MELK.p1 SEQ ID NO: 159 TCTTAGGAACGCCGTACCAGCCGC 24 MMP12 NM_002426S4381/MMP12.f2 SEQ ID NO: 160 CCAACGCTTGCCAAATCCT 19 MMP12 NM_002426S4382/MMP12.r2 SEQ ID NO: 161 ACGGTAGTGACAGCATCAAAACTC 24 MMP12NM_002426 S4383/MMP12.p2 SEQ ID NO: 162 AACCAGCTCTCTGTGACCCCAATT 24 MMP9NM_004994 S0656/MMP9.f1 SEQ ID NO: 163 GAGAACCAATCTCACCGACA 20 MMP9NM_004994 S0657/MMP9.r1 SEQ ID NO: 164 CACCCGAGTGTAACCATAGC 20 MMP9NM_004994 S4760/MMP9.p1 SEQ ID NO: 165 ACAGGTATTCCTCTGCCAGCTGCC 24 MYBL2NM_002466 S3270/MYBL2.f1 SEQ ID NO: 166 GCCGAGATCGCCAAGATG 18 MYBL2NM_002466 S3271/MYBL2.r1 SEQ ID NO: 167 CTTTTGATGGTAGAGTTCCAGTGATTC 27MYBL2 NM_002466 S4742/MYSL2.p1 SEQ ID NO: 168 CAGCATTGTCTGTCCTCCCTGGCA24 NEK2 NM_002497 S4327/NEK2.f1 SEQ ID NO: 169 GTGAGGCAGCGCGACTCT 18NEK2 NM_002497 S4328/NEK2.r1 SEQ ID NO: 170 TGCCAATGGTGTACAACACTTCA 23NEK2 NM_002497 S4329/NEK2.p1 SEQ ID NO: 171 TGCCTTCCCGGGCTGAGGACT 21NME1 NM_000269 S2526/NME1.f3 SEQ ID NO: 172 CCAACCCTGCAGACTCCAA 19 NME1NM_000269 S2527/NME1.r3 SEQ ID NO: 173 ATGTATAATGTTCCTGCCAACTTGTATG 28NME1 NM_000269 S4949/NME1.p3 SEQ ID NO: 174 CCTGGGACCATCCGTGGAGACTTCT 25NPD009 NM_020686 S4474/NP0009.f3 SEQ ID NO: 175 GGCTGTGGCTGAGGCTGTAG 20NPD009 NM_020686 S4475/NP0009.r3 SEQ ID NO: 176 GGAGCATTCGAGGTCAAATCA 21NPD009 NM_020686 S4476/NP0009.p3 SEQ ID NO: 177TTCCCAGAGTGTCTCACCTCCAGCAGAG 28 PCNA NM_002592 S0447/PCNA.f2SEQ ID NO: 178 GAAGGTGTTGGAGGCACTCAAG 22 PCNA NM_002592 S0448/PCNA.r2SEQ ID NO: 179 GGTTTACACCGCTGGAGCTAA 21 PCNA NM_002592 S4784/PCNA.p2SEQ ID NO: 180 ATCCCAGCAGGCCTCGTTGATGAG 24 PR NM_000926 S1336/PR.f6SEQ ID NO: 181 GCATCAGGCTGTCATTATGG 20 PR NM_000926 S1337/PR.r6SEQ ID NO: 182 AGTAGTTGTGCTGCCCTTCC 20 PR NM_000926 S4743/PR.p6SEQ ID NO: 183 TGTCCTTACCTGTGGGAGCTGTAAGGTC 28 PREP NM_002726S1771/PREP.f1 SEQ ID NO: 184 GGGACGGTGTTCACATTCAAG 21 PREP NM_002726S1772/PREP.r1 SEQ ID NO: 185 CAGGATCCCAGAAGTCAATGTTG 23 PREP NM_002726S4929/PREP.p1 SEQ ID NO: 186 TCGCCAGTCTCCCAACTATCGCGT 24 PTTG1 NM_004219S4525/PTTG1.f2 SEQ ID NO: 187 GGCTACTCTGATCTATGTTGATAAGGAA 28 PTTG1NM_004219 S4526/PTTG1.r2 SEQ ID NO: 188 GCTTCAGCCCATCCTTAGCA 20 PTTG1NM_004219 S4527/PTTG1.p2 SEQ ID NO: 189 CACACGGGTGCCTGGTTCTCCA 22 RPLPONM_001002 S0256/RPLPO.f2 SEQ ID NO: 190 CCATTCTATCATCAACGGGTACAA 24RPLPO NM_001002 S0258/RPLPO.r2 SEQ ID NO: 191 TCAGCAAGTGGGAAGGTGTAATC 23RPLPO NM_001002 S4744/RPLPO.p2 SEQ ID NO: 192 TCTCCACAGACAAGGCCAGGACTCG25 SNRPF NM_003095 S4489/SNRPF.f2 SEQ ID NO: 193 GGCTGGTCGGCAGAGAGTAG 20SNRPF NM_003095 S4490/SNRPF.r2 SEQ ID NO: 194 TGAGGAAAGGTTTGGGATTGA 21SNRPF NM_003095 S4491/SNRPF.p2 SEQ ID NO: 195AAACTCATGTAAACCAGGGCCGAATGTTG 29 Src NM_004383 S1820/Src.f2SEQ ID NO: 196 CCTGAACATGAAGGAGCTGA 20

TABLE 4D Src NM_004383 S1621/Src.r2 SEQ ID NO: 197 CATCACGTCTCCGAACTCC19 Src NM_004383 S5034/Src.p2 SEQ ID NO: 198 TCCCGATGGTCTGCAGCAGCT 21STK15 NM_003600 S0794/STK15.f2 SEQ ID NO: 199 CATCTTCCAGGAGGACCACT 20STK15 NM_003600 S0795/STK15.r2 SEQ ID NO: 200 TCCGACCTTCAATCATTTCA 20STK15 NM_003600 S4745/STK15.p2 SEQ ID NO: 201 CTCTGTGGCACCCTGGACTACCTG24 STMY3 NM_005940 S2067/STMY3.f3 SEQ ID NO: 202 CCTGGAGGCTGCAACATACC 20STMY3 NM_005940 S2068/STMY3.r3 SEQ ID NO: 203 TACAATGGCTTTGGAGGATAGCA 23STMY3 NM_005940 S4746/STMY3.p3 SEQ ID NO: 204 ATCCTCCTGAAGCCCTTTTCGCAGC25 SURV NM_001168 S02591SURV.f2 SEQ ID NO: 205 TGTTTTGATTCCCGGGCTTA 20SURV NM_001168 S0261/SURV.r2 SEQ ID NO: 206 CAAAGCTGTCAGCTCTAGCAAAAG 24SURV NM_001168 S4747/SURV.p2 SEQ ID NO: 207 TGCCTTCTTCCTCCCTCACTTCTCACCT28 TFRC NM_003234 S1352/TFRC.f3 SEQ ID NO: 208 GCCAACTGCTTTCATTTGTG 20TFRC NM_003234 S1353/TFRC.r3 SEQ ID NO: 209 ACTCAGGCCCATTTCCTTTA 20 TFRCNM_003234 S4747/TFRC.p3 SEQ ID NO: 210 AGGGATCTGAACCAATACAGAGCAGACA 28TOP2A NM_001067 S0271/TOP2A.f4 SEQ ID NO: 211 AATCCAAGGGGGAGAGTGAT 20TOP2A NM_001067 S0273/TOP2A.r4 SEQ ID NO: 212 GTACAGATTTTGCCCGAGGA 20TOP2A NM_001067 S4777/TOP2A.p4 SEQ ID NO: 213 CATATGGACTTTGACTCAGCTGTGGC26 TS NM_001071 S0280/TS.f1 SEQ ID NO: 214 GCCTCGGTGTGCCTTTCA 18 TSNM_001071 S0282/TS.r1 SEQ ID NO: 215 CGTGATGTGCGCAATCATG 19 TS NM_001071S4780/TS.p1 SEQ ID NO: 216 CATCGCCAGCTACGCCCTGCTC 22

1.-40. (canceled)
 41. A method for analyzing the expression of genes ina human breast cancer patient, comprising: obtaining a fixed,wax-embedded tissue sample from a breast cancer patient; extracting RNAfrom the tissue sample; reverse transcribing RNA transcripts of a set ofgenes consisting of 15-25 genes, wherein the set includes the followinggenes: (a) each of Ki-67, STK15, SURV, CCNB1, MYBL2, and STMY3, and (b)at least one reference gene, to produce cDNAs of the RNA transcripts ofthe set of genes; and amplifying the cDNAs to produce amplicons from thecDNAs for determination of amplicon.
 42. The method of claim 41, whereinthe amplicon levels have been normalized against an amplicon level of anRNA transcript of at least one reference gene in the tissue sample. 43.The method of claim 41, wherein the amplicon levels are threshold cycle(Ct) values.
 44. The method of claim 41, wherein the breast cancerpatient has invasive, ductal carcinoma.
 45. The method of claim 41,wherein the breast cancer patient has node negative and ER positivebreast cancer.
 46. The method of claim 44, wherein the breast cancerpatient has node negative and ER positive breast cancer.
 47. The methodof claim 41, wherein the breast cancer patient has been treated withtamoxifen.
 48. The method of claim 44, wherein the breast cancer patienthas been treated with tamoxifen.
 49. The method of claim 41, wherein thetissue sample comprises core biopsy or fine needle aspirate cells. 50.The method of claim 41, wherein the set of genes consists of 20 genes.51. The method of claim 41, wherein the at least one reference genecomprises GADPH.